Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. Claims 1-20 are presented for examination.
Response to Amendment/Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 4-5, 16 and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The rejection under 103 has been withdrawn since applicant’s amendments and remarks, see page 9-10 filed 05/15/2026, are persuasive and overcome the rejection. The rejection under 103 has been withdrawn in light of the amendments made.
Applicant has argued that the reference of the prior arts, Krishnaswamy (US 2019/0384255) in view of Miklosovic et al. (US 2021/0341901 A1), fail to teach the amended limitation of claims 1, 4-5, 16 and 19. This amendment was added in response to the non-final rejection made by the Office.
The independent claims 1 and 16, amended, “wherein the request comprises request parameters including a type of the device, source data, and a subset of calculation devices”.
However, Krishnaswamy (US 2019/0384255) discloses:
“type of the device”, Par, [0021], [0021] In the example shown in FIG. 1, the system 100 includes one or more sensors 102a and one or more actuators 102b. The sensors 102a and actuators 102b represent components in a process system that may perform any of a wide variety of functions. Par. [0105], variable labeling based on user input on a fault that was detected at level 0. For example, SPE (sensor faults), T.sup.2 (process faults), etc.
“a source data”, in Abstract, the monitoring component includes analyzing streaming incoming process data, which includes process variable and key performance indicators (KPIs), from multiple sources, in real time, to determine an overall health index, determine faults, diagnose and isolate faulty process variables that contribute to the health index, and predict a trend and a magnitude of the health index before failure. Par. [0078], [0079], capable of recognizing faulty process variables and subsequently isolating the fault source; and Pat. [0105], a mechanism to group and create logical data-based models to achieve early fault detection and root cause diagnosis can be achieved.
“a subset of calculation devices”, Par. [0022], Fig. 1, [0023] The system 100 also includes various controllers 106. The controllers 106 can be used in the system 100 to perform various functions in order to control one or more industrial processes. For example, a first set of controllers 106 may use measurements from one or more sensors 102a to control the operation of one or more actuators 102b. These controllers 106 could interact with the sensors 102a, actuators 102b, and other field devices via the I/O module(s) 104. A second set of controllers 106 could be used to optimize the control logic or other operations performed by the first set of controllers. A third set of controllers 106 could be used to perform additional functions. Par. [0026], Operator access to and interaction with the controllers 106 and other components of the system 100 can occur via various operator stations 110. Each operator station 110 could be used to provide information to an operator and receive information from an operator. For example, each operator station 110 could provide information identifying a current state of an industrial process to an operator, such as values of various process variables and warnings, alarms, or other states associated with the industrial process. Each operator station 110 could also receive information affecting how the industrial process is controlled, such as by receiving setpoints for process variables controlled by the controllers 106 or other information that alters or affects how the controllers 106 control the industrial process. Each operator station 110 includes any suitable structure for displaying information to and interacting with an operator.
In addition, Miklosovic et al. (US 2021/0341901 A1) discloses request parameters including a type of the device, source data, and a subset of calculation devices”, in Abstract, analytic engine for motor drives that monitors induction motor conditions for potential failures including rotor faults and stator faults, and obtain runtime signal data from a controller within a drive, derive runtime metrics from the runtime signal data based on an induction motor fault condition, [0002], [0003], [0007], The mechanics connected to each motor have differed amounts of compliance, backlash, friction gravity, torque disturbances, and changing inertia. As a result, the condition of individual mechanical components, machine-to-machine variations, manufacturing tolerances, and wear over time make every machine degrade at a unique rate. [0009], [0032], [0045], output the status, and monitor the induction motor fault (device type) condition based on the status of the induction motor output by the machine learning model; “a source data” in Par. [0045], variable frequency drive 110 supplies to motor 124 of industrial operation 120. In Par.[0037]-[0039], [0048], motor condition monitoring includes detecting stator faults and rotor faults.
In addition, Applicant argues that Krishnaswamy (US 2019/0384255) in view of Miklosovic et al. (US 2021/0341901 A1), fail to disclose the amended limitation “by allocating respective operations of the one or more operations to different ones of the one or more calculation devices prior to execution based at least in part on the request parameters and one or more allocation factors associated with at least a subset of the one or more operations”. This amendment was added in response to the non-final rejection made by the Office, and the argument is persuasive; however, newly cited prior art Lal (US 20240031306 A1) discloses in Abstract, [0004], [0026], [0029], [0039], Fig. 4, 7 and 8, performance and efficiency of a remote computational service or services (e.g., an edge network system) by more intelligently and efficiently assigning or allocating services or tasks to appropriate network devices. The allocation of computer resources may be performed by network compute orchestrator 456 (NCO). NCO 456 may be a software application, hardware device, or a combination of the above suitable for the task of allocating compute resources. user device 414 may be assigned as having its own compute units (e.g., user device 414 may signal local availability of compute units to the NCO). In this case, Table 1 may also list user device 414 as a device of network arrangement of devices that may be allocated compute tasks in another single computer unit request or in split compute unit request.
As a result, the previous rejection has been withdrawn and a new rejection has been made in its place (Krishnaswamy (US 2019/0384255) in view of Miklosovic et al. (US 2021/0341901 A1 further in view of Lal (US 20240031306 A1), see the rejection below for claims 1-20.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
3. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
3.1 Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krishnaswamy (US 2019/0384255) in view of Miklosovic et al. (US 2021/0341901 A1 further in view of Lal (US 20240031306 A1).
Regarding claims 1 and 16, Krishnaswamy discloses method and computer-readable storage media wherein the program instructions, when read and executed by one or more processors, direct the one or more processors to ([0107], [0101], a computer program that is formed from computer readable program code and that is embodied in a computer readable medium. processing system 1402 may comprise a micro-processor that cooperate in executing program instructions);
obtaining a request from a user device for device health information corresponding to a device in an industrial automation environment (Abstract, [0016], [0026], Each operator station 110 could be used to provide information to an operator and receive information from an operator; and provide information for failures in the industrial processes to autonomous predictive health monitoring),
identifying, based on the request, performance metrics associated with the device and one or more operations for producing the device health information from the performance metrics (Fig. 3, [0003], [0004], autonomously identifying and processing signals to detect faults, isolate variables that are a source of the faults, and predict faults that have not yet occurred. And real-time data to determine existence of faults to determine an overall health index for select process variables, diagnosing and isolating, from the select process variables, faulty process variables determined to contribute to the health index, and predicting a trend, a magnitude of the health index, and a contribution thereto of the faulty process variables); environment, wherein the request comprises request parameters including a type of the device ([0021], FIG. 1, the system 100 includes one or more sensors 102a and one or more actuators 102b. The sensors 102a and actuators 102b represent components in a process system that may perform any of a wide variety of functions. Par. [0105], variable labeling based on user input on a fault that was detected at level 0. For example, SPE (sensor faults), T.sup.2 (process faults), etc.),
source data (Abstract, [0078], [0079], [0105], the monitoring component includes analyzing streaming incoming process data, which includes process variable and key performance indicators (KPIs), from multiple sources, in real time, to determine an overall health index, determine faults, diagnose and isolate faulty process variables that contribute to the health index, and predict a trend and a magnitude of the health index before failure, capable of recognizing faulty process variables and subsequently isolating the fault source; and a mechanism to group and create logical data-based models to achieve early fault detection and root cause diagnosis can be achieved, and a subset of calculation devices ( [0022], Fig. 1, [0023] The system 100 also includes various controllers 106. The controllers 106 can be used in the system 100 to perform various functions in order to control one or more industrial processes. For example, a first set of controllers 106 may use measurements from one or more sensors 102a to control the operation of one or more actuators 102b. These controllers 106 could interact with the sensors 102a, actuators 102b, and other field devices via the I/O module(s) 104. A second set of controllers 106 could be used to optimize the control logic or other operations performed by the first set of controllers. A third set of controllers 106 could be used to perform additional functions);
identifying one or more calculation devices (controllers 106) to perform at least a subset of the one or more operations ([0021], [0023]-[0026], the controllers 106 can be used in the system to perform various functions in order to control one or more industrial processes. The sensors 102a and actuators 102b represent components in a process system that may perform any of a wide variety of functions); and
providing the performance metrics, the subset of the one or more operations, and an instruction to perform the subset of the one or more operations on the performance metrics to the one or more calculation devices to produce the device health information ([0004], [0024], [0026], [0029], different controllers 106 could be used to control individual actuators, collections of actuators forming machines, collections of machines forming units, collections of units forming plants, and collections of plants forming an enterprise. For example, each operator station 110 could provide information identifying a current state of an industrial process to an operator, such as values of various process variables and warnings, alarms, or other states associated with the industrial process. Each operator station 110 could also receive information affecting how the industrial process is controlled, such as by receiving setpoints for process variables controlled by the controllers 106 or other information that alters or affects how the controllers 106 control the industrial process. Each operator station 110 includes any suitable structure for displaying information to and interacting with an operator).
Krishnaswamy discloses in par. [0029], identifying a current state of an industrial process to an operator, but fails to discloses performance metrics associated with the device.
However, Miklosovic discloses in Abstract, Par. [0008], [0048], motor based on the runtime metrics and output the status, and monitor the induction motor fault condition based on the status of the induction motor output by the machine learning model.
Krishnaswamy and Miklosovic are analogous art. They relate to health monitoring for a device.
Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify condition monitoring in industrial environments, taught by Miklosovic, incorporated with key performance indicators (KPIs), from multiple sources, taught by Krishnaswamy, in order to perform predictive monitoring to estimate upcoming faults, predict faults that have not yet occurred, with sufficient lead time to correct faults in advance before it accords.
The combination Krishnaswamy and Miklosovic fail to disclose allocating respective operations of the one or more operations to different ones of the one or more calculation devices prior to execution based at least in part on the request parameters and one or more allocation factors associated with at least a subset of the one or more operations.
Lal discloses allocating respective operations of the one or more operations to different ones of the one or more calculation devices prior to execution based at least in part on the request parameters and one or more allocation factors associated with at least a subset of the one or more operations ([0004], [0026],[0029], [0039], Fig. 4, Fig. 7, Fig. 8, performance and efficiency of a remote computational service or services (e.g., an edge network system) by more intelligently and efficiently assigning or allocating services or tasks to appropriate network devices. The allocation of computer resources may be performed by network compute orchestrator 456 (NCO). NCO 456 may be a software application, hardware device, or a combination of the above suitable for the task of allocating compute resources. user device 414 may be assigned as having its own compute units (e.g., user device 414 may signal local availability of compute units to the NCO). In this case, Table 1 may also list user device 414 as a device of network arrangement of devices that may be allocated compute tasks in another single computer unit request or in split compute unit request).
Lal, Krishnaswamy and Miklosovic are analogous art. They relate to health monitoring for a device. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify allocating computation capability, taught by Lal, incorporated with the teaching of Miklosovic and Krishnaswamy, as state above, in order to improve overall performance and efficiency of a remote computational service or services (e.g., an edge network system) by more intelligently and efficiently assigning or allocating services or tasks to appropriate network devices.
Regarding claim 2 and 17, Krishnaswamy obtaining the device health information from the one or more calculation devices ([0026], Fig. 1, Each operator station 110 receive information affecting how the industrial process is controlled, such as by receiving setpoints for process variables controlled by the controllers 106 or other information that alters or affects how the controllers 106 control the industrial process).
Regarding claims 3 and 18, Krishnaswamy discloses providing indications of the device health information to a user interface of the user device for display of the device health information ([0026], each operator station 110 could provide information identifying a current state of an industrial process to an operator, such as values of various process variables and warnings, alarms, or other states associated with the industrial process, Each operator station 110 could also receive information affecting how the industrial process is controlled, such as by receiving setpoints for process variables controlled by the controllers 106 or other information that alters or affects how the controllers 106 control the industrial process. Each operator station 110 includes any suitable structure for displaying information to and interacting with an operator).
Regarding claim 4, 5 and 19, Lal discloses allocating at least one of the one or more operations based on an allocation factor of the one or more allocation factors, wherein the allocation factor includes a latency factor associated with performing the at least one operation and providing resulting device health information for display at the user device and the allocation factor includes an available processing capacity of a calculation device for performing the at least one operations ([0004]-[0006], [0015],[0036], Fig. 4, Fig. 7, Fig. 8, assigning or allocating services or tasks to appropriate network devices. VR/AR service may be assigned a high compute grade with latency requirements for fast delivery of data, while opening a browser may be assigned a low compute grade with a latency requirement for relatively slow delivery of data. Request by accessing a data structure that stores for each of a network arrangement of network devices (a) number and type of available compute units, and (b) latency to the requester device).
Regarding claims 6 and 20, Krishnaswamy discloses the one or more calculation devices comprises an edge server, a cloud server, the device, a controller associated with the device, or a combination thereof ([0023] The system 100 also includes various controllers 106).
Regarding claim 7, Krishnaswamy discloses identifying the one or more calculation devices comprises identifying a first calculation device to perform a first subset of the one or more operations and identifying a second calculation device to perform a second subset of the one or more operations different from the first subset ([0023], The controllers 106 can be used in the system 100 to perform various functions in order to control one or more industrial processes. For example, a first set of controllers 106 may use measurements from one or more sensors 102a to control the operation of one or more actuators 102b. These controllers 106 could interact with the sensors 102a, actuators 102b, and other field devices via the I/O module(s) 104. A second set of controllers 106 could be used to optimize the control logic or other operations performed by the first set of controllers. A third set of controllers 106 could be used to perform additional functions).
Regarding claim 8, Miklosovic discloses performing the subset of the one or more operations on the performance metrics comprises contextualizing each of the performance metrics based on contextualization information specific to each of the performance metrics (Abstract, [0008],[0034], [0035], [0048], a condition monitoring module is configured to obtain runtime signal data from a controller within a drive, derive runtime metrics from the runtime signal data based on an induction motor fault condition. Generating a fault signature comprising a torque reference signal, mechanical speed, electrical frequency, functions of the phase currents such as a normalized negative sequence, and the peak magnitude differences of the frequency responses. Baseline metrics during healthy fault conditions along with runtime metrics during healthy and various levels of degraded fault conditions may be used to construct the machine learning model).
Regarding claim 9, Miklosovic discloses the contextualization information comprises signals indicative of one or more of an electrical value, a mechanical value, and a thermal value associated with the device (0008], [0048], generating a fault signature comprising a torque reference signal, mechanical speed, electrical frequency, functions of the phase currents such as a normalized negative sequence, and the peak magnitude differences of the frequency responses. Baseline metrics during healthy fault conditions along with runtime metrics during healthy and various levels of degraded fault conditions may be used to construct the machine learning model).
Regarding claim 10, Miklosovic discloses performing the one or more operations on the performance metrics further comprises applying a rule set to each of the contextualized performance metrics ([0007], [0030],[0034], [0036], [0044], [0047], Fig. 1, at the drive level, analytic engine may collect data from devices of industrial operation and other sources in various formats. Analytic engine may use collected data to perform condition monitoring, power and energy monitoring, predictive life analysis, load characterization, or similar analyses. At the system level, system analytics aggregate and contextualize information to detect system level fault conditions and/or provide insights related to preventative maintenance, energy diagnostics, system modeling, performance optimization),
wherein the rule set is selectively applied to a respective contextualized performance metric based on a type of the contextualized performance metric ([0052], Capture and configure section 310 comprises signal select 311, capture 312, metric configurations 313, order calculations 314, and timing logic 315. Signal select 311 may select input data based on one or more fault conditions to be monitored. Selectable inputs may include motor current and voltage, data from external connected sensors, internal drive signals (including signals generated from local digital twin models), and additional inputs and signals related to function of an industrial operation).
Regarding claim 11, Krishnaswamy discloses receiving a user input defining the one or more calculation devices ([0022], [0026], [0026] Operator access to and interaction with the controllers 106 and other components of the system 100 can occur via various operator stations 110).
Regarding claim 12, the combination of Miklosovic and Krishnaswamy disclose:
Miklosovic identifying a new device in the industrial automation environment ([0054], a new
device or component is installed);
identifying further performance metrics associated with the new device ([0054], runtime metrics section 330 produces metrics from recent data according to settings specific to the one or more fault conditions being monitored);
identifying a calculation device of the one or more calculation devices to perform at least a subset of the one or more operations on the further performance metrics (Abstract, [0004], [0009], a controller within a drive, derive runtime metrics from the runtime signal data based on an induction motor fault condition); and
performing, based on a new request from the user device, the subset of the one or more operations on the further performance metrics to produce further device health information associated with the new device ((Abstract, [0016], [0026], Each operator station 110 could be used to provide information to an operator and receive information from an operator; and provide information for failures in the industrial processes to autonomous predictive health monitoring).
Krishnaswamy discloses identifying, based on the request, performance metrics associated with the device and one or more operations for producing the device health information from the performance metrics (Fig. 3, [0003], [0004], autonomously identifying and processing signals to detect faults, isolate variables that are a source of the faults, and predict faults that have not yet occurred. And real-time data to determine existence of faults to determine an overall health index for select process variables, diagnosing and isolating, from the select process variables, faulty process variables determined to contribute to the health index, and predicting a trend, a magnitude of the health index, and a contribution thereto of the faulty process variables).
Regarding claim 13, Miklosovic discloses identifying the calculation device to perform at least the subset of the one or more operations on the further performance metrics comprises applying a machine learning model to the new device (abstract, a condition monitoring module is configured to obtain runtime signal data from a controller within a drive, derive runtime metrics from the runtime signal data based on an induction motor fault condition, provide the runtime metrics as input to a machine learning model constructed to identify a status of the induction motor based on the runtime metrics and output the status, and monitor the induction motor fault condition based on the status of the induction motor output by the machine learning model).
Regarding claim 14, Miklosovic discloses the device is a variable-speed drive ([0003],[0029], [0032], monitoring the health of a drive motor, the health of a mechanical load, High-speed drive signals are sent to a programmable logic controller; condition monitoring solutions typically monitor machine parameters such as vibration, temperature, and speeds).
Regarding claim 15, Krishnaswamy discloses the device health information is indicative of a health of the device ([0026], each operator station 110 could provide information identifying a current state of an industrial process to an operator, such as values of various process variables and warnings, health-monitoring, alarms, or other states associated with the industrial process).
Citation Pertinent prior art
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schleiss et al. (US 20190101910 A1) discloses systems and methods for multi-site performance monitoring of process control systems.
Subramanian (US 20200104184 A1) discloses suggested computing resource allocations based on hundreds to thousands (or more) of telemetry data in order to suggest a computing resource allocation. Suggestions made can be accepted or rejected by a resource allocation manager for the edge gateway and the edge server cluster.
Hosek et al. (US 20110173496 A1) discloses distributed approach is different from existing centralized trends referred to as e-diagnostics. In e-diagnostics, all of the data necessary for health monitoring and fault diagnostics are transmitted to a high-level controller, such as the master controller mentioned above, and analyzed at this high level. This approach requires extremely high volumes of data to propagate from the low-level controllers all the way to the high-level controller, often in real time. In addition, the high-level controller needs to store properties of all of the components of the robotized system, such as motor parameters or kinematic and dynamic models of the robots, to be able to process the collected data.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969). a
Conclusion
5. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed Kidest Worku whose telephone number is 571-272-3737. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ali Mohammad can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIDEST WORKU/Primary Examiner, Art Unit 2119
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